The semantic embedding module consists of two fully connected layers and a ReLU activation function. The semantic embedding module maps the deep visual features of the image to the semantic space. Unlike most existing zero-sample classification methods based on embedding[1]}, we do not directly use the depth features of the pre-trained CNN model as its visual representation. It combines Backbone with semantic embedded modules to optimize the network in an end-to-end manner. Specifically, the image features output from Backbone are input to several fully connected layers, and finally activated by ReLU. In order to ensure that the semantic embedding of the output is close to the artificially defined attributes, the binary cross entropy loss function is used to Semantic embedding module is optimized:
\(\mathcal {L}_{B C E}=-\frac{1}{N} \sum _{i=1}^{N}\left(a_{i} \log \tau \left(\zeta _{s e}\left(\phi \left(x_{i}\right)\right)\right)+\left(1-a_{i}\right)\left(1-\log \tau \left(\zeta _{s e}\left(\phi \left(x_{i}\right)\right)\right)\right)\right)\) 
